After data scientists carefully come up with a satisfying machine learning (ML) model, the model must be deployed to be easily accessible for inference by other members of the organization. However, deploying models at scale with optimized …
Read More
Machine Learning is a huge workload, and one of the most demanding when it comes to scaling to thousands (and thousands) of CPUs. Some of the largest workloads customers run in the cloud are deep learning models, which require huge numbers …
Read More
The Ampersand Data Science team had a challenge: their Bayesian statistical models needed more than half a million core-hours of runtime, regularly, if they were to get an answer fast enough for it to be useful to their customers. Scaling …
Read More
Ampersand is a data-driven TV advertising technology company that provides aggregated TV audience impression insights and planning on 42 million households, in every media market, across more than 165 networks and apps and in all dayparts …
Read More
The AWS Graviton 3 will be launched this year as part of the new C7g instance. Inside this chip are some interesting innovations that already have a lot of HPC and AI/ML customers interested. We sat down with Olly Perks, who recently joined …
Read More
In this post, we demonstrate Kubeflow on AWS (an AWS-specific distribution of Kubeflow) and the value it adds over open-source Kubeflow through the integration of highly optimized, cloud-native, enterprise-ready AWS services. Kubeflow is …
Read More
Drug discovery is an expensive proposition, with a $2.6 billion cost over 10 years and just a 12% success rate. AI promises to significantly improve the success rate by finding small molecule hits for undruggable targets. On the forefront …
Read More
The rapid maturation of cloud tools for high-performance workloads in the past several years has made it possible for household names like T-Mobile, Toyota, and Rivian to move their high-performance analytics and AI/ML environments to the …
Read More